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Related Experiment Videos

RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning.

Martin O'Neill¹, Armin R Mikler¹, Saratchandra Indrakanti¹

¹Center for Computational Epidemiology and Response Analysis University of North Texas, Denton, TX USA.

IEEE Transactions on Systems, Man, and Cybernetics. Systems

|November 25, 2014

Summary

We developed the REsponse PLan ANalyzer (RE-PLAN) framework to simplify disaster mitigation planning. This computational tool enables planners to analyze data and run models without coding, making data-driven strategies more accessible.

Keywords:

POD throughput biological emergencies disaster mitigation planning emergency response evacuation planning public health preparedness

Related Experiment Videos

Area of Science:

Computational modeling
Disaster risk reduction
Public health preparedness

Background:

Existing disaster mitigation planning often requires specialized programming or GIS skills, limiting accessibility for policymakers.
There is a need for user-friendly computational tools to support data-driven decision-making in disaster scenarios.

Purpose of the Study:

To present the REsponse PLan ANalyzer (RE-PLAN) framework, a modular system for quantitative disaster analysis.
To demonstrate the application of RE-PLAN through Bio-E, a tool for analyzing ad hoc clinic feasibility in biological emergencies.
To enable planners and policymakers to utilize complex data and computational models via an accessible interface.

Main Methods:

Development of the RE-PLAN framework, comprising modules for quantitative disaster scenario analysis.
Implementation of the Bio-E tool using the RE-PLAN framework for biological emergency preparedness.
Design of a point-and-click interface for harnessing disparate data and executing computational models.

Main Results:

The RE-PLAN framework facilitates the creation of specialized disaster mitigation tools.
The Bio-E tool demonstrates the framework's capability in analyzing the feasibility of ad hoc treatment clinics.
The framework allows users without technical expertise to perform complex quantitative analyses.

Conclusions:

The RE-PLAN framework effectively bridges the gap between complex computational analysis and practical disaster planning.
User-friendly tools like Bio-E, built on RE-PLAN, enhance accessibility to data-driven mitigation strategies.
This approach empowers planners and policymakers to improve preparedness for various disaster scenarios.